基于高斯混合模型的消费贷款违约概率预测
Forecasting Probability of Default for Consumer Loan Management with Gaussian Mixture Models
摘要：
信用评分是全球金融机构和信贷机构用于财务决策的重要工具。本文提出了一种基于高斯混合模型（GMM）的贷款违约概率预测方法。将相似的客户彼此聚类，我们的模型将每个组的健康概率关联起来。此外，我们的基于GMM的模型将个体样本与聚类概率关联，然后根据其与GMM聚类的关系估计每个个体的违约概率。我们为银行和非银行金融机构的风险管理者和决策者提供应用程序，通过向那些违约概率低于决策阈值的人提供贷款，使利润最大化并减轻预期损失。我们的模式有很多优点。首先，它给出了每个申请人的信用状况的概率视图，而不是二元分类，因此为金融决策者提供了更多的信息。第二，我们的基于GMM的违约概率计算的训练集上的预期损失是非常接近的实际损失，第三，我们的方法是计算效率。
英文摘要：
Credit scoring is an essential tool used by global financial institutions and credit lenders for financial decision making. In this paper, we introduce a new method based on Gaussian Mixture Model (GMM) to forecast the probability of default for individual loan applicants. Clustering similar customers with each other, our model associates a probability of being healthy to each group. In addition, our GMM-based model probabilistically associates individual samples to clusters, and then estimates the probability of default for each individual based on how it relates to GMM clusters. We provide applications for risk managers and decision makers in banks and non-bank financial institutions to maximize profit and mitigate the expected loss by giving loans to those who have a probability of default below a decision threshold. Our model has a number of advantages. First, it gives a probabilistic view of credit standing for each individual applicant instead of a binary classification and therefore provides more information for financial decision makers. Second, the expected loss on the train set calculated by our GMM-based default probabilities is very close to the actual loss, and third, our approach is computationally efficient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
